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Gamgam G, Yıldırım Z, Kabakçıoğlu A, Gurvit H, Demiralp T, Acar B. Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108290. [PMID: 38954916 DOI: 10.1016/j.cmpb.2024.108290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 05/09/2024] [Accepted: 06/16/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, which have poor specificity for diagnostic purposes. Following recent advances in machine learning, deep neural networks, particularly Graph Neural Network (GNN) based approaches, have found applications in brain research as well. The majority of existing applications of GNNs employ a single network (uni-modal or structure/function unified), despite the widely accepted view that there is a nontrivial interdependence between the brain's structural connectivity and the neural activity patterns, which is hypothesized to be disrupted in ADD. This disruption is quantified as a discrepancy score by the proposed "structure-function discrepancy learning network" (sfDLN) and its distribution is studied over the spectrum of clinical cognitive decline. The measured discrepancy score is utilized as a diagnostic biomarker and is compared with state-of-the-art diagnostic classifiers. METHODS sfDLN is a GNN with a siamese architecture built on the hypothesis that the mismatch between structural and functional connectivity patterns increases over the cognitive decline spectrum, starting from subjective cognitive impairment (SCI), passing through a mid-stage mild cognitive impairment (MCI), and ending up with ADD. The structural brain connectome (sNET) built using diffusion MRI-based tractography and the novel, sparse (lean) functional brain connectome (ℓNET) built using fMRI are input to sfDLN. The siamese sfDLN is trained to extract connectome representations and a discrepancy (dissimilarity) score that complies with the proposed hypothesis and is blindly tested on an MCI group. RESULTS The sfDLN generated structure-function discrepancy scores show high disparity between ADD and SCI subjects. Leave-one-out experiments of SCI-ADD classification over a cohort of 42 subjects reach 88% accuracy, surpassing state-of-the-art GNN-based classifiers in the literature. Furthermore, a blind assessment over a cohort of 46 MCI subjects confirmed that it captures the intermediary character of the MCI group. GNNExplainer module employed to investigate the anatomical determinants of the observed discrepancy confirms that sfDLN attends to cortical regions neurologically relevant to ADD. CONCLUSION In support of our hypothesis, the harmony between the structural and functional organization of the brain degrades with increasing cognitive decline. This discrepancy, shown to be rooted in brain regions neurologically relevant to ADD, can be quantified by sfDLN and outperforms state-of-the-art GNN-based ADD classification methods when used as a biomarker.
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Affiliation(s)
- Gurur Gamgam
- VAVlab, Department of Electrical And Electronics Eng., Bogazici University, Istanbul, 34342, Turkiye
| | - Zerrin Yıldırım
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, Istanbul, 34093, Turkiye
| | | | - Hakan Gurvit
- Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, 34093, Turkiye
| | - Tamer Demiralp
- Hulusi Behçet Life Sciences Research Lab., Istanbul University, Istanbul, 34093, Turkiye
| | - Burak Acar
- VAVlab, Department of Electrical And Electronics Eng., Bogazici University, Istanbul, 34342, Turkiye.
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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024:10.1038/s41583-024-00846-6. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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Zhang H, Cao P, Mak HKF, Hui ES. The structural-functional-connectivity coupling of the aging brain. GeroScience 2024; 46:3875-3887. [PMID: 38443539 PMCID: PMC11226573 DOI: 10.1007/s11357-024-01106-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/07/2024] Open
Abstract
Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain's resilience against aging-related cognitive decline. This study aims to examine the relationship between structural-functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study. The SFC is a recently proposed biomarker that reflects the extent to which anatomical brain connections can predict coordinated neural activity. After controlling for the effect of age, sex, and years of education, global SFC, as well as the intra-network SFC of the dorsolateral somatomotor and dorsal attention networks, and the inter-network SFC between dorsolateral somatomotor and frontoparietal networks decreased with age. The global SFC decreased with total cognitive score. There were significant interaction effects between years of education versus white matter hyperintensities and between years of education versus cerebral microbleeds on inter-network SFC. Enlarged perivascular space in basal ganglia was associated with higher inter-network SFC. Our results suggest that cognitive ability is associated with brain coupling at the global level and cognitive reserve with brain coupling at the inter-functional-brain-cluster level with interaction effect from white matter hyperintensities and cerebral microbleed in a cohort of healthy elderlies.
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Affiliation(s)
- Hui Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Henry K F Mak
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
- Alzheimer's Disease Research Network, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
| | - Edward S Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.
- CU Lab for AI in Radiology (CLAIR), The Chinese University of Hong Kong, Hong Kong, China.
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Sanda P, Hlinka J, van den Berg M, Skoch A, Bazhenov M, Keliris GA, Krishnan GP. Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression. PLoS Comput Biol 2024; 20:e1012099. [PMID: 38843298 PMCID: PMC11185486 DOI: 10.1371/journal.pcbi.1012099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 06/18/2024] [Accepted: 04/23/2024] [Indexed: 06/19/2024] Open
Abstract
Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.
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Affiliation(s)
- Pavel Sanda
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
| | - Monica van den Berg
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Georgios A. Keliris
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece
| | - Giri P. Krishnan
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
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Batista AF, Khan KA, Papavergi MT, Lemere CA. The Importance of Complement-Mediated Immune Signaling in Alzheimer's Disease Pathogenesis. Int J Mol Sci 2024; 25:817. [PMID: 38255891 PMCID: PMC10815224 DOI: 10.3390/ijms25020817] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/05/2024] [Accepted: 01/07/2024] [Indexed: 01/24/2024] Open
Abstract
As an essential component of our innate immune system, the complement system is responsible for our defense against pathogens. The complement cascade has complex roles in the central nervous system (CNS), most of what we know about it stems from its role in brain development. However, in recent years, numerous reports have implicated the classical complement cascade in both brain development and decline. More specifically, complement dysfunction has been implicated in neurodegenerative disorders, such as Alzheimer's disease (AD), which is the most common form of dementia. Synapse loss is one of the main pathological hallmarks of AD and correlates with memory impairment. Throughout the course of AD progression, synapses are tagged with complement proteins and are consequently removed by microglia that express complement receptors. Notably, astrocytes are also capable of secreting signals that induce the expression of complement proteins in the CNS. Both astrocytes and microglia are implicated in neuroinflammation, another hallmark of AD pathogenesis. In this review, we provide an overview of previously known and newly established roles for the complement cascade in the CNS and we explore how complement interactions with microglia, astrocytes, and other risk factors such as TREM2 and ApoE4 modulate the processes of neurodegeneration in both amyloid and tau models of AD.
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Affiliation(s)
- André F. Batista
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (A.F.B.); (K.A.K.); (M.-T.P.)
| | - Khyrul A. Khan
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (A.F.B.); (K.A.K.); (M.-T.P.)
| | - Maria-Tzousi Papavergi
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (A.F.B.); (K.A.K.); (M.-T.P.)
- School for Mental Health and Neuroscience (MHeNs), Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Cynthia A. Lemere
- Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA; (A.F.B.); (K.A.K.); (M.-T.P.)
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Chen H, Hu Z, Ke Z, Xu Y, Bai F, Liu Z. Aberrant Multimodal Connectivity Pattern Involved in Default Mode Network and Limbic Network in Amyotrophic Lateral Sclerosis. Brain Sci 2023; 13:brainsci13050803. [PMID: 37239275 DOI: 10.3390/brainsci13050803] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/07/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that progressively affects bulbar and limb function. Despite increasing recognition of the disease as a multinetwork disorder characterized by aberrant structural and functional connectivity, its integrity agreement and its predictive value for disease diagnosis remain to be fully elucidated. In this study, we recruited 37 ALS patients and 25 healthy controls (HCs). High-resolution 3D T1-weighted imaging and resting-state functional magnetic resonance imaging were, respectively, applied to construct multimodal connectomes. Following strict neuroimaging selection criteria, 18 ALS and 25 HC patients were included. Network-based statistic (NBS) and the coupling of grey matter structural-functional connectivity (SC-FC coupling) were performed. Finally, the support vector machine (SVM) method was used to distinguish the ALS patients from HCs. Results showed that, compared with HCs, ALS individuals exhibited a significantly increased functional network, predominantly encompassing the connections between the default mode network (DMN) and the frontoparietal network (FPN). The increased structural connections predominantly involved the inter-regional connections between the limbic network (LN) and the DMN, the salience/ventral attention network (SVAN) and FPN, while the decreased structural connections mainly involved connections between the LN and the subcortical network (SN). We also found increased SC-FC coupling in DMN-related brain regions and decoupling in LN-related brain regions in ALS, which could differentiate ALS from HCs with promising capacity based on SVM. Our findings highlight that DMN and LN may play a vital role in the pathophysiological mechanism of ALS. Additionally, SC-FC coupling could be regarded as a promising neuroimaging biomarker for ALS and shows important clinical potential for early recognition of ALS individuals.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Zheqi Hu
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
- Medical School of Nanjing University, Nanjing University, Nanjing 210093, China
| | - Zhihong Ke
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
- Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 211166, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
| | - Zhuo Liu
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing 210008, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing 210008, China
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Wang J, Xue Y, He Y, Quan H, Zhang J, Gao YQ. Characterization of network hierarchy reflects cell state specificity in genome organization. Genome Res 2023; 33:247-260. [PMID: 36828586 PMCID: PMC10069467 DOI: 10.1101/gr.277206.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/31/2023] [Indexed: 02/26/2023]
Abstract
Dynamic chromatin structure acts as the regulator of transcription program in crucial processes including cancer and cell development, but a unified framework for characterizing chromatin structural evolution remains to be established. Here, we performed graph inferences on Hi-C data sets and derived the chromatin contact networks. We discovered significant decreases in information transmission efficiencies in chromatin of colorectal cancer (CRC) and T-cell acute lymphoblastic leukemia (T-ALL) compared to corresponding normal controls through graph statistics. Using network embedding in the Poincaré disk, the hierarchy depths of chromatin from CRC and T-ALL patients were found to be significantly shallower compared to their normal controls. A reverse trend of change in chromatin structure was observed during early embryo development. We found tissue-specific conservation of hierarchy order in chromatin contact networks. Our findings reveal the top-down hierarchy of chromatin organization, which is significantly attenuated in cancer.
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Affiliation(s)
- Jingyao Wang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Yue Xue
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Yueying He
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Hui Quan
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Jun Zhang
- Changping Laboratory, Beijing, 102206, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China; .,Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China.,Changping Laboratory, Beijing, 102206, China
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Wang X, Si K, Gu W, Wang X. Mitigating effects and mechanisms of Tai Chi on mild cognitive impairment in the elderly. Front Aging Neurosci 2023; 14:1028822. [PMID: 36760710 PMCID: PMC9906996 DOI: 10.3389/fnagi.2022.1028822] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
Mild cognitive impairment (MCI) is a major public health concern that endangers health and decreases the quality of life of the elderly around the world. A recent clinical guideline has recommended regular exercise (twice per week) for patients with MCI as part of an overall approach to management. Tai Chi, a form of light-to-moderate-intensity mind-body exercise, is particularly suitable for seniors. This review aims to summarize epidemiological studies related to the effects of Tai Chi on symptom remission in older adults with MCI and reveal the potential mechanisms. Evidence suggested that Tai Chi can improve cognitive functions and alleviate the accompanying symptoms of MCI in the elderly potentially by activating the expression of signals in different brain regions, altering their connectivity, increasing the brain volume, and modulating brain-derived neurotropic and inflammation factors. Studies comparing various types of Tai Chi may contribute to the identification of paradigms that have appropriate intensities and difficulty and exert good effects on older people with MCI. In addition, studies are warranted to determine the frequency and duration of training that can optimize the beneficial effects of Tai Chi on MCI.
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Affiliation(s)
- Xin Wang
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, China
| | - Keyi Si
- Department of Military Health Statistics, Naval Medical University, Shanghai, China
| | - Wei Gu
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, China
| | - Xueqiang Wang
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
- Department of Rehabilitation Medicine, Shanghai Shangti Orthopaedic Hospital, Shanghai, China
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Wang M, Xu B, Hou X, Shi Q, Zhao H, Gui Q, Wu G, Dong X, Xu Q, Shen M, Cheng Q, Feng H. Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment. Front Aging Neurosci 2023; 15:1153496. [PMID: 37122379 PMCID: PMC10140296 DOI: 10.3389/fnagi.2023.1153496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 03/28/2023] [Indexed: 05/02/2023] Open
Abstract
Objective Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data. Methods The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed. Results Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI. Conclusion Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.
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Derbie AY, Dejenie MA, Zegeye TG. Visuospatial representation in patients with mild cognitive impairment: Implication for rehabilitation. Medicine (Baltimore) 2022; 101:e31462. [PMID: 36343037 PMCID: PMC9646670 DOI: 10.1097/md.0000000000031462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Behavioral and neurophysiological experiments have demonstrated that distinct and common cognitive processes and associated neural substrates maintain allocentric and egocentric spatial representations. This review aimed to provide evidence from previous behavioral and neurophysiological studies on collating cognitive processes and associated neural substrates and linking them to the state of visuospatial representations in patients with mild cognitive impairment (MCI). Even though MCI patients showed impaired visuospatial attentional processing and working memory, previous neuropsychological experiments in MCI largely emphasized memory impairment and lacked substantiating evidence of whether memory impairment could be associated with how patients with MCI encode objects in space. The present review suggests that impaired memory capacity is linked to impaired allocentric representation in MCI patients. This review indicates that further research is needed to examine how the decline in visuospatial attentional resources during allocentric coding of space could be linked to working memory impairment.
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Affiliation(s)
- Abiot Y. Derbie
- Applied Cognitive Neuroscience Laboratory, Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong
- Department of Psychology, Bahir Dar University, Bahir Dar, Ethiopia
- *Correspondence: Abiot Y. Derbie, Department of Psychology, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia (e-mail: )
| | | | - Tsigie G. Zegeye
- Department of Special Needs, Bahir Dar University, Bahir Dar, Ethiopia
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Resting-State fMRI Whole Brain Network Function Plasticity Analysis in Attention Deficit Hyperactivity Disorder. Neural Plast 2022; 2022:4714763. [PMID: 36199291 PMCID: PMC9529483 DOI: 10.1155/2022/4714763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/08/2022] [Indexed: 12/03/2022] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in children, which is related to inattention and hyperactivity. These symptoms are associated with abnormal interactions of brain networks. We used resting-state functional magnetic resonance imaging (rs-fMRI) based on the graph theory to explore the topology property changes of brain networks between an ADHD group and a normal group. The more refined AAL_1024 atlas was used to construct the functional networks with high nodal resolution, for detecting more subtle changes in brain regions and differences among groups. We compared altered topology properties of brain network between the groups from multilevel, mainly including modularity at mesolevel. Specifically, we analyzed the similarities and differences of module compositions between the two groups. The results found that the ADHD group showed stronger economic small-world network property, while the clustering coefficient was significantly lower than the normal group; the frontal and occipital lobes showed smaller node degree and global efficiency between disease statuses. The modularity results also showed that the module number of the ADHD group decreased, and the ADHD group had short-range overconnectivity within module and long-range underconnectivity between modules. Moreover, modules containing long-range connections between the frontal and occipital lobes disappeared, indicating that there was lack of top-down control information between the executive control region and the visual processing region in the ADHD group. Our results suggested that these abnormal regions were related to executive control and attention deficit of ADHD patients. These findings helped to better understand how brain function correlates with the ADHD symptoms and complement the fewer modularity elaboration of ADHD research.
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Xu T, Dragomir A, Liu X, Yin H, Wan F, Bezerianos A, Wang H. An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis. Front Neuroinform 2022; 16:907942. [PMID: 36051853 PMCID: PMC9426721 DOI: 10.3389/fninf.2022.907942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/16/2022] [Indexed: 11/22/2022] Open
Abstract
With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.
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Affiliation(s)
- Tao Xu
- The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
| | - Andrei Dragomir
- The N1 Institute, National University of Singapore, Singapore, Singapore
| | - Xucheng Liu
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, Macao SAR, China
| | - Haojun Yin
- The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
| | - Feng Wan
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, Macao SAR, China
| | - Anastasios Bezerianos
- Hellenic Institute of Transport (HIT), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece
| | - Hongtao Wang
- The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
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13
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Xu X, Xu S, Han L, Yao X. Coupling analysis between functional and structural brain networks in Alzheimer's disease. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:8963-8974. [PMID: 35942744 DOI: 10.3934/mbe.2022416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.
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Affiliation(s)
- Xia Xu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Song Xu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liting Han
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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14
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Yang F, Jiang X, Yue F, Wang L, Boecker H, Han Y, Jiang J. Exploring dynamic functional connectivity alterations in the preclinical stage of Alzheimer's disease: an exploratory study from SILCODE. J Neural Eng 2022; 19. [PMID: 35147522 DOI: 10.1088/1741-2552/ac542d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/08/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION Exploring functional connectivity (FC) alterations is important for the understanding of underlying neuronal network alterations in subjective cognitive decline (SCD). The objective of this study was to prove that dynamic FC can better reflect the changes of brain function in individuals with SCD compared to static FC, and further to explore the association between FC alterations and amyloid pathology in the preclinical stage of Alzheimer's disease (AD). METHODS 101 normal control (NC) subjects, 97 SCDs, and 55 cognitive impairment (CI) subjects constituted the whole-cohort. Of these, 29 NCs and 52 SCDs with amyloid images were selected as the sub-cohort. First, independent components (ICs) were identified by independent component analysis and static and dynamic FC were calculated by pairwise correlation coefficient between ICs. Second, FC alterations were identified through group comparison, and seed-based dynamic FC analysis was done. Analysis of variance (ANOVA) was used to compare the seed-based dynamic FC maps and measure the group or amyloid effects. Finally, correlation analysis was conducted between the altered dynamic FC and amyloid burden. RESULTS The results showed that 42 ICs were revealed. Significantly altered dynamic FC included those between the salience/ventral attention network, the default mode network, and the visual network. Specifically, the thalamus/caudate (IC 25) drove the hub role in the group differences. In the seed-based dynamic FC analysis, the dynamic FC between the thalamus/caudate and the middle temporal/frontal gyrus was observed to be higher in the SCD and CI groups. Moreover, a higher dynamic FC between the thalamus/caudate and visual cortex was observed in the amyloid positive group. Finally, the altered dynamic FC was associated with the amyloid global standardized uptake value ratio (SUVr). CONCLUSION Our findings suggest SCD-related alterations could be more reflected by dynamic FC than static FC, and the alterations are associated with global SUVr.
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Affiliation(s)
- Fan Yang
- Shanghai University, Shangda Road, Baoshan district, Shanghai, Shanghai, 200444, CHINA
| | - Xueyan Jiang
- Hainan University, Meilan District, Haikou City, Hainan Province, Haikou, 570288, CHINA
| | - Feng Yue
- Hainan University, Meilan District, Haikou City, Hainan Province, Haikou, 570288, CHINA
| | - Luyao Wang
- Shanghai University, Shangda road, Baoshan district, shanghai, Shanghai, 200444, CHINA
| | - Henning Boecker
- University Hospital Bonn, Positron Emission Tomography (PET) Group, Bonn, Germany, Bonn, Nordrhein-Westfalen, 53127, GERMANY
| | - Ying Han
- Hainan University, Meilan District, Haikou City, Hainan Province, Haikou, 570288, CHINA
| | - Jiehui Jiang
- Shanghai University, Shangda road, Baoshan district, Shanghai, Shanghai, 200444, CHINA
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15
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Hall GR, Boehm-Sturm P, Dirnagl U, Finke C, Foddis M, Harms C, Koch SP, Kuchling J, Madan CR, Mueller S, Sassi C, Sotiropoulos SN, Trueman RC, Wallis MD, Yildirim F, Farr TD. Long-Term Connectome Analysis Reveals Reshaping of Visual, Spatial Networks in a Model With Vascular Dementia Features. Stroke 2022; 53:1735-1745. [PMID: 35105183 PMCID: PMC9022688 DOI: 10.1161/strokeaha.121.036997] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Connectome analysis of neuroimaging data is a rapidly expanding field that offers the potential to diagnose, characterize, and predict neurological disease. Animal models provide insight into biological mechanisms that underpin disease, but connectivity approaches are currently lagging in the rodent.
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Affiliation(s)
- Gerard R Hall
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.)
| | - Philipp Boehm-Sturm
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Ulrich Dirnagl
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.).,German Center for Neurodegenerative Diseases, Berlin Site, Germany (U.D.)
| | - Carsten Finke
- Department of Neurology, Charité-Universitätsmedizin Berlin, Germany. (C.F., J.K.).,Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Germany (C.F.)
| | - Marco Foddis
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Christoph Harms
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Stefan Paul Koch
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Joseph Kuchling
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité-Universitätsmedizin Berlin (J.K.).,Department of Neurology, Charité-Universitätsmedizin Berlin, Germany. (C.F., J.K.)
| | | | - Susanne Mueller
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Celeste Sassi
- Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, United Kingdom (S.N.S.).,Centre for Functional MRI of the Brain, University of Oxford, United Kingdom (S.N.S.)
| | - Rebecca C Trueman
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.)
| | - Marcus D Wallis
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.)
| | - Ferah Yildirim
- corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.).,NeuroCure Cluster of Excellence and Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Germany. (F.Y.)
| | - Tracy D Farr
- School of Life Sciences, University of Nottingham, United Kingdom (G.R.H., R.C.T., M.D.W., T.D.F.).,Department of Experimental Neurology, Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., T.D.F.).,corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Germany. NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Germany. (P.B.-S., U.D., M.F., C.H., S.P.K., S.M., C.S., F.Y., T.D.F.)
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16
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Wei C, Gong S, Zou Q, Zhang W, Kang X, Lu X, Chen Y, Yang Y, Wang W, Jia L, Lyu J, Shan B. A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:774607. [PMID: 34938173 PMCID: PMC8687449 DOI: 10.3389/fnagi.2021.774607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/08/2021] [Indexed: 11/17/2022] Open
Abstract
Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI. Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores. Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively). Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.
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Affiliation(s)
- Cuibai Wei
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Geriatric Cognitive Disorders, Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Shuting Gong
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.,School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Qi Zou
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.,College of Integrated Traditional Chinese and Western Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Wei Zhang
- Institute of High Energy Physics, Chinese Academy of Sciences, Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
| | - Xuechun Kang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.,School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xinliang Lu
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.,School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yufei Chen
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yuting Yang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.,School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Wei Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Longfei Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Jihui Lyu
- Center for Cognitive Disorders, Beijing Geriatric Hospital, Beijing, China
| | - Baoci Shan
- Institute of High Energy Physics, Chinese Academy of Sciences, Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China
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17
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Relationship between Amyloid-β Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer's Disease. Brain Sci 2021; 11:brainsci11111535. [PMID: 34827535 PMCID: PMC8615711 DOI: 10.3390/brainsci11111535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 01/02/2023] Open
Abstract
Previous studies have demonstrated that the accumulation of amyloid-β (Aβ) pathologies has distinctive stage-specific effects on the structural and functional brain networks along the Alzheimer's disease (AD) continuum. A more comprehensive account of both types of brain network may provide a better characterization of the stage-specific effects of Aβ pathologies. A potential candidate for this joint characterization is the coupling between the structural and functional brain networks (SC-FC coupling). We therefore investigated the effect of Aβ accumulation on global SC-FC coupling in patients with mild cognitive impairment (MCI), AD, and healthy controls. Patients with MCI were dichotomized according to their level of Aβ pathology seen in 18F-flutemetamol PET-CT scans-namely, Aβ-negative and Aβ-positive. Our results show that there was no difference in global SC-FC coupling between different cohorts. During the prodromal AD stage, there was a significant negative correlation between the level of Aβ pathology and the global SC-FC coupling of MCI patients with positive Aβ, but no significant correlation for MCI patients with negative Aβ. During the AD dementia stage, the correlation between Aβ pathology and global SC-FC coupling in patients with AD was positive. Our results suggest that Aβ pathology has distinctive stage-specific effects on global coupling between the structural and functional brain networks along the AD continuum.
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18
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D'Souza NS, Nebel MB, Crocetti D, Robinson J, Wymbs N, Mostofsky SH, Venkataraman A. Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations. Neuroimage 2021; 241:118388. [PMID: 34271159 PMCID: PMC8528511 DOI: 10.1016/j.neuroimage.2021.118388] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/05/2021] [Accepted: 07/10/2021] [Indexed: 11/27/2022] Open
Abstract
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.
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Affiliation(s)
- N S D'Souza
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA.
| | - M B Nebel
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA
| | - D Crocetti
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA
| | - J Robinson
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA
| | - N Wymbs
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA
| | - S H Mostofsky
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, USA
| | - A Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
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19
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Scangos KW, Khambhati AN, Daly PM, Owen LW, Manning JR, Ambrose JB, Austin E, Dawes HE, Krystal AD, Chang EF. Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology. Front Hum Neurosci 2021; 15:746499. [PMID: 34744662 PMCID: PMC8566975 DOI: 10.3389/fnhum.2021.746499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/02/2021] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.
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Affiliation(s)
- Katherine Wilson Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Ankit N. Khambhati
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Patrick M. Daly
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lucy W. Owen
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Jeremy R. Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Josiah B. Ambrose
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Everett Austin
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Heather E. Dawes
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew D. Krystal
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F. Chang
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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20
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Shu P, Zhu H, Jin W, Zhou J, Tong S, Sun J. The Resilience and Vulnerability of Human Brain Networks Across the Lifespan. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1756-1765. [PMID: 34410925 DOI: 10.1109/tnsre.2021.3105991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Resilience, the ability for a system to maintain its basic functionality when suffering from lesions, is a critical property for human brain, especially in the brain aging process. This study adopted a novel metric of network resilience, the Resilience Index (RI), to assess human brain resilience with three different lifespan datasets. Based on the structural brain networks constructed from diffusion tensor imaging (DTI), we observed an inverted-U relationship between RI and age, that is, RI increased during development and early adulthood, reached a peak at about 35 years old, and then decreased during aging, which suggested that brain resilience could be quantified by RI. Furthermore, we studied brain network vulnerability by the decreases in RI when virtual lesions occurred to nodes (i.e., brain regions) or edges (i.e., structural brain connectivity). We found that the strong edges were markedly vulnerable, and the homotopic edges were the most prominent representatives of vulnerable edges. In other words, an arbitrary attack on homotopic edges would have a high probability to degrade brain network resilience. These findings suggest the change of human brain resilience across the lifespan and provide a new perspective for exploring human brain vulnerability.
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21
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Chen H, Li W, Sheng X, Ye Q, Zhao H, Xu Y, Bai F. Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study. Eur Radiol 2021; 32:448-459. [PMID: 34109489 DOI: 10.1007/s00330-021-08080-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/13/2021] [Accepted: 05/19/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD. METHODS One hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers. RESULTS We found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity. CONCLUSION This study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD. KEY POINTS • Multimodal brain networks improve the detection accuracy of SCD. • Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.
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Affiliation(s)
- Haifeng Chen
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Weikai Li
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Qing Ye
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China. .,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China. .,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. .,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China.
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22
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Cao Y, Yang H, Zhou Z, Cheng Z, Zhao X. Abnormal Default-Mode Network Homogeneity in Patients With Mild Cognitive Impairment in Chinese Communities. Front Neurol 2021; 11:569806. [PMID: 33643176 PMCID: PMC7905225 DOI: 10.3389/fneur.2020.569806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 12/23/2020] [Indexed: 11/15/2022] Open
Abstract
Background and Objective: Current evidence suggests that abnormalities within the default-mode network (DMN) play a key role in the broad-scale cognitive problems that characterize mild cognitive impairment (MCI). However, little is known about the alterations of DMN network homogeneity (NH) in MCI. Methods: Resting-state functional magnetic resonance imaging scans (rs-fMRI) were collected from 38 MCI patients and 69 healthy controls matched for age, gender, and education. NH approach was employed to analyze the imaging dataset. Cognitive performance was measured with the Chinese version of Alzheimer's disease assessment scale-Cognitive subscale (ADAS-Cog). Results: Two groups have no significant differences between demographic factors. And mean ADAS-Cog score in MCI was 12.02. MCI patients had significantly lower NH values than controls in the right anterior cingulate cortex and significantly higher NH values in the ventral medial prefrontal cortex(vmPFC) than those in healthy controls. No significant correlations were found between abnormal NH values and ADAS-Cog in the patients. Conclusions: These findings provide further evidence that abnormal NH of the DMN exists in MCI, and highlight the significance of DMN in the pathophysiology of cognitive problems occurring in MCI.
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Affiliation(s)
- Yuping Cao
- Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China.,China National Clinical Research Center on Mental Disorders, Changsha, China.,China National Technology Institute on Mental Disorders, Changsha, China.,Hunan Technology Institute of Psychiatry, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Huan Yang
- Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China.,China National Clinical Research Center on Mental Disorders, Changsha, China.,China National Technology Institute on Mental Disorders, Changsha, China.,Hunan Technology Institute of Psychiatry, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, China
| | - Zhenhe Zhou
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Zaohuo Cheng
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Xingfu Zhao
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
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23
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Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning. Biomedicines 2021; 9:biomedicines9010082. [PMID: 33467174 PMCID: PMC7830949 DOI: 10.3390/biomedicines9010082] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/11/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia, and depression is a risk factor for developing AD. Epidemiological studies provide a clinical correlation between late-life depression (LLD) and AD. Depression patients generally remit with no residual symptoms, but LLD patients demonstrate residual cognitive impairment. Due to the lack of effective treatments, understanding how risk factors affect the course of AD is essential to manage AD. Advances in neuroimaging, including resting-state functional MRI (fMRI), have been used to address neural systems that contribute to clinical symptoms and functional changes across various psychiatric disorders. Resting-state fMRI studies have contributed to understanding each of the two diseases, but the link between LLD and AD has not been fully elucidated. This review focuses on three crucial and well-established networks in AD and LLD and discusses the impacts on cognitive decline, clinical symptoms, and prognosis. Three networks are the (1) default mode network, (2) executive control network, and (3) salience network. The multiple properties emphasized here, relevant for the hypothesis of the linkage between LLD and AD, will be further developed by ongoing future studies.
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24
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Xue C, Sun H, Hu G, Qi W, Yue Y, Rao J, Yang W, Xiao C, Chen J. Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. Front Neurosci 2020; 14:575652. [PMID: 33177982 PMCID: PMC7593791 DOI: 10.3389/fnins.2020.575652] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/25/2020] [Indexed: 01/06/2023] Open
Abstract
Background Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) were considered to be a continuum of Alzheimer’s disease (AD) spectrum. The abnormal topological architecture and rich-club organization in the brain functional network can reveal the pathology of the AD spectrum. However, few studies have explored the disrupted patterns of diverse club organizations and the combination of rich- and diverse-club organizations in SCD and aMCI. Methods We collected resting-state functional magnetic resonance imaging data of 19 SCDs, 29 aMCIs, and 28 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative. Graph theory analysis was used to analyze the network metrics and rich- and diverse-club organizations simultaneously. Results Compared with HC, the aMCI group showed altered small-world and network efficiency, whereas the SCD group remained relatively stable. The aMCI group showed reduced rich-club connectivity compared with the HC. In addition, the aMCI group showed significantly increased feeder connectivity and decreased local connectivity of the diverse club compared with the SCD group. The overlapping nodes of the rich club and diverse club showed a significant difference in nodal efficiency and shortest path length (Lp) between groups. Notably, the Lp values of overlapping nodes in the SCD and aMCI groups were significantly associated with episodic memory. Conclusion The present study demonstrates that the network properties of SCD and aMCI have varying degrees of damage. The combination of the rich club and the diverse club can provide a novel insight into the pathological mechanism of the AD spectrum. The altered patterns in overlapping nodes might be potential biomarkers in the diagnosis of the AD spectrum.
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Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Haiting Sun
- Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University (Air Force Medical University), Xi'an, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenjie Yang
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
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25
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Zhang L, Ni H, Yu Z, Wang J, Qin J, Hou F, Yang A. Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment. Front Neurosci 2020; 14:558434. [PMID: 33100958 PMCID: PMC7556272 DOI: 10.3389/fnins.2020.558434] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 09/04/2020] [Indexed: 01/13/2023] Open
Abstract
Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer’s disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.
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Affiliation(s)
- Lulu Zhang
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Huangjing Ni
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Zhinan Yu
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Jiaolong Qin
- Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Fengzhen Hou
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Albert Yang
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
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26
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Breastfeeding improves dynamic reorganization of functional connectivity in preterm infants: a temporal brain network study. Med Biol Eng Comput 2020; 58:2805-2819. [PMID: 32945999 DOI: 10.1007/s11517-020-02244-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 07/31/2020] [Indexed: 10/23/2022]
Abstract
Substantial evidences have shown the benefits of breastfeeding to infants in terms of better nutrition and neurodevelopmental outcome. However, the relationship between brain development and feeding in preterm infants, who are physiologically and developmentally immature at birth, is only beginning to be quantitatively assessed, coinciding with the recent advent of neuroimaging techniques. In the current work, we studied a sample of 50 preterm infants-born between 29 and 33 weeks (32.20 ± 0.89 weeks) of gestational age, where 30 of them were breastfed and the remaining 20 were formula-fed. Resting-state functional magnetic resonance imaging (fMRI) was recorded around term-equivalent age (40.00 ± 1.31 weeks, range 39-44 weeks) using a 1.5-T scanner under sedation condition. Temporal brain networks were estimated by the correlation of sliding time-window time courses among regions of a predefined atlas. Through our newly introduced temporal efficiency approach, we examined, for the first time, the 3D spatiotemporal architecture of the temporal brain network. We found prominent temporal small-world properties in both groups, suggesting the arrangement of dynamic functional connectivity permits effective coordination of various brain regions for efficient information transfer over time at both local and global levels. More importantly, we showed that breastfed preterm infants exhibited greater temporal global efficiency in comparison with formula-fed preterm infants. Specifically, we found localized elevation of temporal nodal properties in the right temporal gyrus and bilateral caudate. Taken together, these findings provide new evidence to support the notion that breast milk promotes early brain development and cognitive function, which may have neurobiological and public health implications for parents and pediatricians.Breastfeeding has long been recognized to have beneficial effect on early neurodevelopment in infants. However, the influence of breastfeeding on reorganization of functional connectivity in preterm infants are largely unknown. To this end, we utilized our recently developed temporal brain network analysis framework to investigate the dynamic reorganization of brain functional connectivity in preterm infants fed with breast milk. We found that beyond an optimal temporal small-world topology, breastfed preterm infants exhibited improved network efficiency at both global and regional levels in comparisons with those of formula-fed infants. Graphical abstract: Breastfeeding has long been recognized to have beneficial effect on early neurodevelopment in infants. However, the influence of breastfeeding on reorganization of brain functional connectivity in preterm infants are largely unknown. To this end, we utilized our recently developed temporal brain network analysis framework to investigate the dynamic reorganization of functional connectivity in preterm infants fed with breast milk. We found that beyond an optimal temporal small-world topology, breastfed preterm infants exhibited improved network efficiency at both global and regional levels in comparisons with those of formula-fed infants.
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Sherman DS, Durbin KA, Ross DM. Meta-Analysis of Memory-Focused Training and Multidomain Interventions in Mild Cognitive Impairment. J Alzheimers Dis 2020; 76:399-421. [PMID: 32508325 DOI: 10.3233/jad-200261] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Meta-analysis examining the efficacy of cognitive interventions on neuropsychological outcomes have suggested interventions that focus on memory may actually provide greater benefit against the cognitive declines associated with mild cognitive impairment (MCI). However, it remains unclear if memory-based training would be more effective at addressing the cognitive deficits associated with MCI than multidomain forms of intervention. OBJECTIVE A meta-analytic review and subgroup analysis was conducted to examine the effects of cognitive training in individuals diagnosed with MCI and to compare the efficacy of memory-based training with multidomain interventions. METHODS A total of 32 randomized controlled trials met inclusion criteria for the meta-analysis, which included 9 studies on memory-focused training and 17 studies on multidomain interventions. RESULTS We found significant, large effects for memory-focused training (Hedges' g observed = 0.947; 95% CI [-1.668, 3.562]; Z = 2.517; p = 0.012) and significant, moderate effects for multidomain interventions (Hedges' g observed = 0.420; 95% CI [-0.4491, 1.2891]; Z = 3.525; p < 0.001). A subgroup analysis found significant point estimates for memory-based forms of training and multidomain interventions, with memory-based forms of content yielding significantly greater summary effects than multidomain interventions (SMD Z = 2.162; p = 0.031, two-tailed; all outcomes). There was no difference between effect sizes when comparing outcomes limited to its respective domain. CONCLUSION Overall, these findings suggest that, while both interventions were beneficial, treatment interventions that were strictly memory-based were more effective at improving cognition in individuals diagnosed with MCI than interventions that targeted multiple cognitive domains.
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Affiliation(s)
- Dale S Sherman
- Department of Physical Medicine & Rehabilitation, Cedars-Sinai Medical Center, Los Angeles, CA, USA.,Rossier School of Education, University of Southern California, Los Angeles, CA, USA
| | - Kelly A Durbin
- Department of Physical Medicine & Rehabilitation, Cedars-Sinai Medical Center, Los Angeles, CA, USA.,Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - David M Ross
- Department of Physical Medicine & Rehabilitation, Cedars-Sinai Medical Center, Los Angeles, CA, USA.,Department of Psychology, Loma Linda University, Loma Linda, CA, USA
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28
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Yeung MK, Chan AS. Functional near-infrared spectroscopy reveals decreased resting oxygenation levels and task-related oxygenation changes in mild cognitive impairment and dementia: A systematic review. J Psychiatr Res 2020; 124:58-76. [PMID: 32120065 DOI: 10.1016/j.jpsychires.2020.02.017] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 02/06/2023]
Abstract
Nuclear medicine and functional magnetic resonance imaging studies have shown that mild cognitive impairment (MCI) and dementia, including Alzheimer's disease (AD), are characterized by changes in cerebral blood flow. This article reviews the application of an alternative method, functional near-infrared spectroscopy (fNIRS), to the study of cerebral oxygenation changes in MCI and dementia. We synthesized 36 fNIRS studies that examined hemodynamic changes during both the resting state and the execution of tasks of word retrieval, memory, motor control, and visuospatial perception in MCI and dementia. This qualitative review reveals that (amnestic) MCI and AD patients have disrupted frontal and long-range connectivity in the resting state compared to individuals with normal cognition (NC). These patients also exhibit reduced frontal oxygenation changes in various cognitive domains. The review also shows that disrupted connectivity and decreased frontal oxygenation levels/changes are more severe in AD than in (amnestic) MCI, confirming that MCI is an intermediate stage between NC and dementia. Thus, there is reduced resting frontal perfusion, which is greater than expected for age, and a lack of frontal compensatory responses to functional decline across cognitive operations (i.e., word retrieval and memory functioning) in MCI and AD. These indices might potentially serve as perfusion- or oxygenation-based biomarkers for MCI/dementia. To expand the utility of fNIRS for MCI and dementia, further studies that measure tissue oxygenation in a wider range of brain regions and cognitive domains, compare different MCI and dementia types, and correlate changes in cerebral oxygenation over time with disease progression are needed.
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Affiliation(s)
- Michael K Yeung
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Agnes S Chan
- Neuropsychology Laboratory, Department of Psychology, The Chinese University of Hong Kong, Hong Kong SAR, China; Chanwuyi Research Center for Neuropsychological Well-being, The Chinese University of Hong Kong, Hong Kong SAR, China.
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29
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Kurmukov A, Mussabaeva A, Denisova Y, Moyer D, Jahanshad N, Thompson PM, Gutman BA. Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering. Brain Connect 2020; 10:183-194. [PMID: 32264696 PMCID: PMC7247040 DOI: 10.1089/brain.2019.0722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.
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Affiliation(s)
- Anvar Kurmukov
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Higher School of Economics, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Ayagoz Mussabaeva
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Yulia Denisova
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Daniel Moyer
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Boris A Gutman
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
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30
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Costumero V, d'Oleire Uquillas F, Diez I, Andorrà M, Basaia S, Bueichekú E, Ortiz-Terán L, Belloch V, Escudero J, Ávila C, Sepulcre J. Distance disintegration delineates the brain connectivity failure of Alzheimer's disease. Neurobiol Aging 2020; 88:51-60. [PMID: 31941578 PMCID: PMC7085436 DOI: 10.1016/j.neurobiolaging.2019.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 01/03/2023]
Abstract
Alzheimer's disease (AD) is associated with brain network dysfunction. Network-based investigations of brain connectivity have mainly focused on alterations in the strength of connectivity; however, the network breakdown in AD spectrum is a complex scenario in which multiple pathways of connectivity are affected. To integrate connectivity changes that occur under AD-related conditions, here we developed a novel metric that computes the connectivity distance between cortical regions at the voxel level (or nodes). We studied 114 individuals with mild cognitive impairment, 24 with AD, and 27 healthy controls. Results showed that areas of the default mode network, salience network, and frontoparietal network display a remarkable network separation, or greater connectivity distances, from the rest of the brain. Furthermore, this greater connectivity distance was associated with lower global cognition. Overall, the investigation of AD-related changes in paths and distances of connectivity provides a novel framework for characterizing subjects with cognitive impairment; a framework that integrates the overall network topology changes of the brain and avoids biases toward unreferenced connectivity effects.
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Affiliation(s)
- Víctor Costumero
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Catalonia, Spain; Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | | | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neurotechnology Laboratory, Tecnalia Health Department, Basque Country, Spain
| | - Magi Andorrà
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center of Neuroimmunology, Department of Neurology, Hospital Clinic of Barcelona, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Catalonia, Spain
| | - Silvia Basaia
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neuroimaging Research Unit Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | - Laura Ortiz-Terán
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Joaquin Escudero
- Department of Neurology, General Hospital of Valencia, Valencia, Valencian Community, Spain
| | - César Ávila
- Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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Increased intrinsic default-mode network activity as a compensatory mechanism in aMCI: a resting-state functional connectivity MRI study. Aging (Albany NY) 2020; 12:5907-5919. [PMID: 32238610 PMCID: PMC7185142 DOI: 10.18632/aging.102986] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/24/2020] [Indexed: 11/25/2022]
Abstract
Numerous studies have investigated the differences in the mean functional connectivity (FC) strength between amnestic mild cognitive impairment (aMCI) patients and normal subjects using resting-state functional magnetic resonance imaging. However, whether the mean FC is increased, decreased or unchanged in aMCI patients compared to normal controls remains unclear. Two factors might lead to inconsistent results: the determination of regions of interest and the reliability of the FC. We explored differences in FC and the degree centrality (Dc) constructed by the bootstrap method, between and within networks (default-mode network (DN), frontoparietal control network (CN), dorsal attention network (AN)), and resulting from a hierarchical-clustering algorithm. The mean FC within the DN and CN was significantly increased (P < 0.05, uncorrected) in patients. Significant increases (P < 0.05, uncorrected) in the mean FC were found in patients between DN and CN and between DN and AN. Five pairs of FC (false discovery rate corrected) and the Dc of six regions (Bonferroni corrected) displayed a significant increase in patients. Lower cognitive ability was significantly associated with a greater increase in the Dc of the left superior temporal sulcus. Our results demonstrate that the early dysfunctions in aMCI disease are mainly compensatory impairments.
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Gao Z, Feng Y, Ma C, Ma K, Cai Q, and for the Alzheimer’s Disease Neuroimaging Initiative. Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph. Curr Alzheimer Res 2020; 17:69-79. [DOI: 10.2174/1567205017666200213100607] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 09/16/2019] [Accepted: 01/20/2020] [Indexed: 02/07/2023]
Abstract
Background:
Alzheimer's Disease (AD) is a progressive neurodegenerative disease with insidious
onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological
information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD.
Methods:
We innovatively used a Visibility Graph (VG) to construct the time-dependent brain networks
as well as functional connectivity network to investigate the underlying dynamics of AD brain based on
functional magnetic resonance imaging. There were 32 AD patients and 29 Normal Controls (NCs) from
the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, the VG method mapped the
time series of single brain region into networks. By extracting topological properties of the networks, the
most significant features were selected as discriminant features into a supporting vector machine for
classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD
brain, functional connectivity among different brain regions was calculated based on the correlation of
regional degree sequences.
Results:
According to the topology abnormalities exploration of local complex networks, we found several
abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions.
The accuracy of characteristics of the brain regions extracted from local complex networks was
88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right
middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in
AD.
Conclusion:
These results would be helpful in revealing the underlying pathological mechanism of the
disease.
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Affiliation(s)
- Zhongke Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Yanhua Feng
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Chao Ma
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Kai Ma
- Principal Researcher at Tencent, Guangdong, China
| | - Qing Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
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Cao R, Wang X, Gao Y, Li T, Zhang H, Hussain W, Xie Y, Wang J, Wang B, Xiang J. Abnormal Anatomical Rich-Club Organization and Structural-Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease. Front Neurol 2020; 11:53. [PMID: 32117016 PMCID: PMC7013042 DOI: 10.3389/fneur.2020.00053] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 01/14/2020] [Indexed: 12/17/2022] Open
Abstract
Emerging research indicates interruptions in the wiring organization of the brain network in Mild cognitive impairment (MCI) and Alzheimer's disease (AD). Due to the important role of rich-club organization in distinguishing abnormalities of AD patients and the close relationship between structural connectivity (SC) and functional connectivity (FC), our study examined whether changes in SC-FC coupling and the relationship with abnormal rich-club organizations during the development of diseases may contribute to the pathophysiology of AD. Structural diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) were performed in 38 normal controls (NCs), 40 MCI patients and 19 AD patients. Measures of the rich-club structure and its role in global structural-functional coupling were administered. Our study found decreased levels of feeder and local connectivity in MCI and AD patients, which were the main contributing factors to the lower efficiency of the brain structural network. Another important finding was that we have more accurately characterized the changing pattern of functional brain dynamics. The enhanced coupling between SC and FC in MCI and AD patients might be due to disruptions in optimal structural organization. More interestingly, we also found increases in the SC-FC coupling for feeder and local connections in MCI and AD patients. SC-FC coupling also showed significant differences between MCI and AD patients, mainly between the abnormal feeder connections. The connection density and coupling strength were significantly correlated with clinical metrics in patients. The present findings enhanced our understanding of the neurophysiologic mechanisms associated with MCI and AD.
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Affiliation(s)
- Rui Cao
- College of Software, Taiyuan University of Technology, Taiyuan, China
| | - Xin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yuan Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Waqar Hussain
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jing Wang
- Department of Health management, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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Zhang Y, Dai Z, Chen Y, Sim K, Sun Y, Yu R. Altered intra- and inter-hemispheric functional dysconnectivity in schizophrenia. Brain Imaging Behav 2020; 13:1220-1235. [PMID: 30094555 DOI: 10.1007/s11682-018-9935-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Despite convergent evidence suggesting that schizophrenia is a disorder of brain dysconnectivity, it remains unclear whether intra- or inter-hemispheric deficits or their combination underlie the dysconnection. This study examined the source of the functional dysconnection in schizophrenia. Resting-state fMRI was performed in 66 patients with schizophrenia and 73 matched healthy controls. Functional brain networks were constructed for each participant and further partitioned into intra- and inter-hemispheric connections. We examined how schizophrenia altered the intra-hemispheric topological properties and the inter-hemispheric nodal strength. Although several subcortical and cingulate regions exhibited hemispheric-independent aberrations of regional efficiency, the optimal small-world properties in the hemispheric networks and their lateralization were preserved in patients. A significant deficit in the inter-hemispheric connectivity was revealed in most of the hub regions, leading to an inter-hemispheric hypo-connectivity pattern in patients. These abnormal intra- and inter-hemispheric network organizations were associated with the clinical features of schizophrenia. The patients in the present study received different medications. These findings provide new insights into the nature of dysconnectivity in schizophrenia, highlighting the dissociable processes between the preserved intra-hemispheric network topology and altered inter-hemispheric functional connectivity.
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Affiliation(s)
- Yuan Zhang
- Key Laboratory for Biomedical Engineering of the Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, 310000, China.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Zhongxiang Dai
- Department of Computer Science, National University of Singapore, Singapore, Singapore
| | - Yu Chen
- School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health, Singapore, Singapore.,Department of Research, Institute of Mental Health, Singapore, Singapore
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of the Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, 310000, China.
| | - Rongjun Yu
- Department of Psychology, National University of Singapore, Block AS4, #02-07, 9 Arts Link, Singapore, 117570, Singapore. .,Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore.
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35
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Changes in functional and structural brain connectome along the Alzheimer's disease continuum. Mol Psychiatry 2020; 25:230-239. [PMID: 29743583 DOI: 10.1038/s41380-018-0067-8] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 02/05/2018] [Accepted: 03/06/2018] [Indexed: 12/13/2022]
Abstract
The aim of this study was two-fold: (i) to investigate structural and functional brain network architecture in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), stratified in converters (c-aMCI) and non-converters (nc-aMCI) to AD; and to assess the relationship between healthy brain network functional connectivity and the topography of brain atrophy in patients along the AD continuum. Ninety-four AD patients, 47 aMCI patients (25 c-aMCI within 36 months) and 53 age- and sex-matched healthy controls were studied. Graph analysis and connectomics assessed global and local, structural and functional topological network properties and regional connectivity. Healthy topological features of brain regions were assessed based on their connectivity with the point of maximal atrophy (epicenter) in AD and aMCI patients. Brain network graph analysis properties were severely altered in AD patients. Structural brain network was already altered in c-aMCI patients relative to healthy controls in particular in the temporal and parietal brain regions, while functional connectivity did not change. Structural connectivity alterations distinguished c-aMCI from nc-aMCI cases. In both AD and c-aMCI, the point of maximal atrophy was located in left hippocampus (disease-epicenter). Brain regions most strongly connected with the disease-epicenter in the healthy functional connectome were also the most atrophic in both AD and c-aMCI patients. Progressive degeneration in the AD continuum is associated with an early breakdown of anatomical brain connections and follows the strongest connections with the disease-epicenter. These findings support the hypothesis that the topography of brain connectional architecture can modulate the spread of AD through the brain.
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Shaw SB, Dhindsa K, Reilly JP, Becker S. Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics. Neural Comput 2019; 31:2177-2211. [DOI: 10.1162/neco_a_01229] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.
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Affiliation(s)
- Saurabh Bhaskar Shaw
- Neuroscience Graduate Program, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Kiret Dhindsa
- Research and High Performance Computing, McMaster University, Hamilton, ON L8S 4L8, Canada, and Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
| | - James P. Reilly
- Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada, and Department of Electrical and Computer Engineering and McMaster School of Biomedical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Suzanna Becker
- Department of Psychology Neuroscience and Behaviour, McMaster University, Hamilton, ON L8S 4L8, Canada, and Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada
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Wang H, Xu G, Wang X, Sun C, Zhu B, Fan M, Jia J, Guo X, Sun L. The Reorganization of Resting-State Brain Networks Associated With Motor Imagery Training in Chronic Stroke Patients. IEEE Trans Neural Syst Rehabil Eng 2019; 27:2237-2245. [DOI: 10.1109/tnsre.2019.2940980] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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38
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Wang B, Miao L, Niu Y, Cao R, Li D, Yan P, Guo H, Yan T, Wu J, Xiang J. Abnormal Functional Brain Networks in Mild Cognitive Impairment and Alzheimer's Disease: A Minimum Spanning Tree Analysis. J Alzheimers Dis 2019; 65:1093-1107. [PMID: 30149457 DOI: 10.3233/jad-180603] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Alzheimer's disease (AD) disrupts the topological architecture of whole-brain connectivity. Minimum spanning tree (MST), which captures the most important connections in a network, has been considered an unbiased method for brain network analysis. However, the alterations in the MST of functional brain networks during the progression of AD remain unclear. Here, we performed an MST analysis to examine the alterations in functional networks among normal controls (NCs), mild cognitive impairment (MCI) patients, and AD patients. We identified substantial differences in the connections among the three groups. The maximum betweenness centrality, leaf number, and tree hierarchy of the MSTs showed significant group differences, indicating a more star-like topology in the MCI patients and a more line-like topology in the NCs and AD patients. These findings may correspond to changes in the core of the functional brain networks. For nodal properties (degree and betweenness centrality), we determined that brain regions around the cingulate gyrus, occipital lobes, subcortex, and inferior temporal gyrus showed significant differences among the three groups and contributed to the global topological alterations. The leaf number and tree hierarchy, as well as the nodal properties, were significantly correlated with clinical features in the MCI and AD patients, which demonstrated that more star-to-line topology changes were associated with worse cognitive performance in these patients. These findings indicated that MST properties could capture slight alterations in network topology, particularly for the differences between NCs and MCI patients, and may be applicable as neuroimaging markers of the early stage of AD.
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Synchronization dependent on spatial structures of a mesoscopic whole-brain network. PLoS Comput Biol 2019; 15:e1006978. [PMID: 31013267 PMCID: PMC6499430 DOI: 10.1371/journal.pcbi.1006978] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 05/03/2019] [Accepted: 03/26/2019] [Indexed: 11/20/2022] Open
Abstract
Complex structural connectivity of the mammalian brain is believed to underlie the versatility of neural computations. Many previous studies have investigated properties of small subsystems or coarse connectivity among large brain regions that are often binarized and lack spatial information. Yet little is known about spatial embedding of the detailed whole-brain connectivity and its functional implications. We focus on closing this gap by analyzing how spatially-constrained neural connectivity shapes synchronization of the brain dynamics based on a system of coupled phase oscillators on a mammalian whole-brain network at the mesoscopic level. This was made possible by the recent development of the Allen Mouse Brain Connectivity Atlas constructed from viral tracing experiments together with a new mapping algorithm. We investigated whether the network can be compactly represented based on the spatial dependence of the network topology. We found that the connectivity has a significant spatial dependence, with spatially close brain regions strongly connected and distal regions weakly connected, following a power law. However, there are a number of residuals above the power-law fit, indicating connections between brain regions that are stronger than predicted by the power-law relationship. By measuring the sensitivity of the network order parameter, we show how these strong connections dispersed across multiple spatial scales of the network promote rapid transitions between partial synchronization and more global synchronization as the global coupling coefficient changes. We further demonstrate the significance of the locations of the residual connections, suggesting a possible link between the network complexity and the brain’s exceptional ability to swiftly switch computational states depending on stimulus and behavioral context. In a previous study, a data-driven large-scale model of mouse brain connectivity was constructed. This mouse brain connectivity model is estimated by a simplified model which only takes in account anatomy and distance dependence of connection strength which is best fit by a power law. The distance dependence model captures the connection strengths of the mouse whole-brain network well. But can it capture the dynamics? In this study, we show that a small number of connections which are missed by the simple spatial model lead to significant differences in dynamics. The presence of a small number of strong connections over longer distances increases sensitivity of synchronization to perturbations. Unlike the data-driven network, the network without these long-range connections, as well as the network in which these long range connections are shuffled, lose global synchronization while maintaining localized synchrony, underlining the significance of the exact topology of these distal connections in the data-driven brain network.
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van Montfort SJT, van Dellen E, Stam CJ, Ahmad AH, Mentink LJ, Kraan CW, Zalesky A, Slooter AJC. Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis. NEUROIMAGE-CLINICAL 2019; 23:101809. [PMID: 30981940 PMCID: PMC6461601 DOI: 10.1016/j.nicl.2019.101809] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/25/2019] [Accepted: 03/31/2019] [Indexed: 01/05/2023]
Abstract
Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome. Delirium is consistently associated with functional network impairments. Risk factors are associated with lower structural connectivity strength. Risk factors are associated with a less efficient structural network organization. Structural impairments make the functional network more vulnerable to deterioration. Functional network disintegration seems to be a final common pathway for delirium.
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Affiliation(s)
- S J T van Montfort
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - E van Dellen
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - A H Ahmad
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Psychology, Utrecht University, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands
| | - L J Mentink
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - C W Kraan
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - A Zalesky
- Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - A J C Slooter
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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Li F, Wang J, Liao Y, Yi C, Jiang Y, Si Y, Peng W, Yao D, Zhang Y, Dong W, Xu P. Differentiation of Schizophrenia by Combining the Spatial EEG Brain Network Patterns of Rest and Task P300. IEEE Trans Neural Syst Rehabil Eng 2019; 27:594-602. [DOI: 10.1109/tnsre.2019.2900725] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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42
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Zhang X, Yu X, Bao Q, Yang L, Sun Y, Qi P. Multimodal neuroimaging study reveals dissociable processes between structural and functional networks in patients with subacute intracerebral hemorrhage. Med Biol Eng Comput 2019; 57:1285-1295. [DOI: 10.1007/s11517-019-01953-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 01/16/2019] [Indexed: 12/19/2022]
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Straathof M, Sinke MRT, Dijkhuizen RM, Otte WM. A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains. J Cereb Blood Flow Metab 2019; 39:189-209. [PMID: 30375267 PMCID: PMC6360487 DOI: 10.1177/0271678x18809547] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/28/2018] [Indexed: 12/29/2022]
Abstract
The mammalian brain is composed of densely connected and interacting regions, which form structural and functional networks. An improved understanding of the structure-function relation is crucial to understand the structural underpinnings of brain function and brain plasticity after injury. It is currently unclear how functional connectivity strength relates to structural connectivity strength. We obtained an overview of recent papers that report on correspondences between quantitative functional and structural connectivity measures in the mammalian brain. We included network studies in which functional connectivity was measured with resting-state fMRI, and structural connectivity with either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven of the 28 included studies showed a positive structure-function relationship. Large inter-study variations were found comparing functional connectivity strength with either quantitative diffusion-based (correlation coefficient (r) ranges: 0.18-0.82) or neuronal tracer-based structural connectivity measures (r = 0.24-0.74). Two functional datasets demonstrated lower structure-function correlations with neuronal tracer-based (r = 0.22 and r = 0.30) than with diffusion-based measures (r = 0.49 and r = 0.65). The robust positive quantitative structure-function relationship supports the hypothesis that structural connectivity provides the hardware from which functional connectivity emerges. However, methodological differences between the included studies complicate the comparison across studies, which emphasize the need for validation and standardization in brain structure-function studies.
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Affiliation(s)
- Milou Straathof
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Michel RT Sinke
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
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44
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Lin SY, Lin CP, Hsieh TJ, Lin CF, Chen SH, Chao YP, Chen YS, Hsu CC, Kuo LW. Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease. Neuroimage Clin 2019; 22:101680. [PMID: 30710870 PMCID: PMC6357901 DOI: 10.1016/j.nicl.2019.101680] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 12/03/2018] [Accepted: 01/20/2019] [Indexed: 01/08/2023]
Abstract
Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.
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Affiliation(s)
- Shih-Yen Lin
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan; Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
| | - Chen-Pei Lin
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Tsung-Jen Hsieh
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Chung-Fen Lin
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Sih-Huei Chen
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Yi-Ping Chao
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yong-Sheng Chen
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
| | - Chih-Cheng Hsu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan; Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.
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45
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Sánchez-Catasús CA, Willemsen A, Boellaard R, Juarez-Orozco LE, Samper-Noa J, Aguila-Ruiz A, De Deyn PP, Dierckx R, Medina YI, Melie-Garcia L. Episodic memory in mild cognitive impairment inversely correlates with the global modularity of the cerebral blood flow network. Psychiatry Res Neuroimaging 2018; 282:73-81. [PMID: 30419408 DOI: 10.1016/j.pscychresns.2018.11.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 09/23/2018] [Accepted: 11/02/2018] [Indexed: 12/25/2022]
Abstract
Cerebral blood flow (CBF) SPECT is an interesting methodology to study brain connectivity in mild cognitive impairment (MCI) since it is accessible worldwide and can be used as a biomarker of neuronal injury in MCI. In CBF SPECT, connectivity is grounded in group-based correlation networks. Therefore, topological metrics derived from the CBF correlation network cannot be used to support diagnosis and prognosis individually. However, methods to extract the individual patient contribution to topological metrics of group-based correlation networks were developed although not yet applied to MCI patients. Here, we investigate whether the episodic memory of 24 amnestic MCI patients correlates with individual patient contributions to topological metrics of the CBF correlation network. We first compared topological metrics of the MCI group network with the network corresponding to 26 controls. Metrics that showed significant differences were then used for the individual patient contribution analysis. We found that the global network modularity was increased while global efficiency decreased in the MCI network compared to the control. Most importantly, we found that episodic memory inversely correlates with the patient contribution to the global network modularity, which highlights the potential of this approach to develop a CBF connectivity-based biomarker at the individual level.
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Affiliation(s)
- Carlos A Sánchez-Catasús
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands; Center for Neurological Restoration (CIREN), Ave. 25, No. 15 805, Playa, La Habana 11300, Cuba.
| | - Antoon Willemsen
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Luis Eduardo Juarez-Orozco
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Juan Samper-Noa
- Hospital Carlos J. Finlay, Ave. 31, Playa, La Habana 11400, Cuba; Cuban Neuroscience Center, Ave. 25, No. 15007, Playa, La Habana 11600, Cuba
| | - Angel Aguila-Ruiz
- Center for Neurological Restoration (CIREN), Ave. 25, No. 15 805, Playa, La Habana 11300, Cuba
| | - Peter Paul De Deyn
- Department of Neurology and Alzheimer Research Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands; University of Antwerp, Institute Born-Bunge, Laboratory of Neurochemistry and Behaviour, Universiteitsplein 1, Antwerpen BE-2610, Belgium
| | - Rudi Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands
| | - Yasser Iturria Medina
- Cuban Neuroscience Center, Ave. 25, No. 15007, Playa, La Habana 11600, Cuba; McConnell Brain Imaging Center, Montreal Neurological Institute, 3801 University Street, Montréal, Quebec H3A 2B4, Canada
| | - Lester Melie-Garcia
- Cuban Neuroscience Center, Ave. 25, No. 15007, Playa, La Habana 11600, Cuba; Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), Mont-Paisible 16, Lausanne CH-1011, Switzerland
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46
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Dragomir A, Vrahatis AG, Bezerianos A. A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework. IEEE J Biomed Health Inform 2018; 23:14-25. [PMID: 30080151 DOI: 10.1109/jbhi.2018.2863202] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.
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47
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Kesler SR, Acton P, Rao V, Ray WJ. Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer's disease. Netw Neurosci 2018; 2:241-258. [PMID: 30215035 PMCID: PMC6130552 DOI: 10.1162/netn_a_00048] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 02/14/2018] [Indexed: 12/19/2022] Open
Abstract
Neurodegeneration in Alzheimer's disease (AD) is associated with amyloid-beta peptide accumulation into insoluble amyloid plaques. The five-familial AD (5XFAD) transgenic mouse model exhibits accelerated amyloid-beta deposition, neuronal dysfunction, and cognitive impairment. We aimed to determine whether connectome properties of these mice parallel those observed in patients with AD. We obtained diffusion tensor imaging and resting-state functional magnetic resonance imaging data for four transgenic and four nontransgenic male mice. We constructed both structural and functional connectomes and measured their topological properties by applying graph theoretical analysis. We compared connectome properties between groups using both binarized and weighted networks. Transgenic mice showed higher characteristic path length in weighted structural connectomes and functional connectomes at minimum density. Normalized clustering and modularity were lower in transgenic mice across the upper densities of the structural connectome. Transgenic mice also showed lower small-worldness index in higher structural connectome densities and in weighted structural networks. Hyper-correlation of structural and functional connectivity was observed in transgenic mice compared with nontransgenic controls. These preliminary findings suggest that 5XFAD mouse connectomes may provide useful models for investigating the molecular mechanisms of AD pathogenesis and testing the effectiveness of potential treatments.
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Affiliation(s)
- Shelli R Kesler
- Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paul Acton
- Neurodegeneration Consortium, Institute for Applied Cancer Science, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vikram Rao
- Department of Neuro-oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - William J Ray
- Neurodegeneration Consortium, Institute for Applied Cancer Science, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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48
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Zhuo Z, Mo X, Ma X, Han Y, Li H. Identifying aMCI with functional connectivity network characteristics based on subtle AAL atlas. Brain Res 2018; 1696:81-90. [PMID: 29729253 DOI: 10.1016/j.brainres.2018.04.042] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 04/28/2018] [Accepted: 04/30/2018] [Indexed: 11/17/2022]
Abstract
PURPOSE To investigate the subtle functional connectivity alterations of aMCI based on AAL atlas with 1024 regions (AAL_1024 atlas). MATERIALS AND METHODS Functional MRI images of 32 aMCI patients (Male/Female: 15/17, Ages: 66.8 ± 8.36 y) and 35 normal controls (Male/Female:13/22, Ages: 62.4 ± 8.14 y) were obtained in this study. Firstly, functional connectivity networks were constructed by Pearson's Correlation based on the subtle AAL_1024 atlas. Then, local and global network parameters were calculated from the thresholding functional connectivity matrices. Finally, multiple-comparison analysis was performed on these parameters to find the functional network alterations of aMCI. And furtherly, a couple of classifiers were adopted to identify the aMCI by using the network parameters. RESULTS More subtle local brain functional alterations were detected by using AAL_1024 atlas. And the predominate nodes including hippocampus, inferior temporal gyrus, inferior parietal gyrus were identified which was not detected by AAL_90 atlas. The identification of aMCI from normal controls were significantly improved with the highest accuracy (98.51%), sensitivity (100%) and specificity (97.14%) compared to those (88.06%, 84.38% and 91.43% for the highest accuracy, sensitivity and specificity respectively) obtained by using AAL_90 atlas. CONCLUSION More subtle functional connectivity alterations of aMCI could be found based on AAL_1024 atlas than those based on AAL_90 atlas. Besides, the identification of aMCI could also be improved.
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Affiliation(s)
- Zhizheng Zhuo
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Xiao Mo
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Xiangyu Ma
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China.
| | - Haiyun Li
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
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49
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Taya F, Dimitriadis SI, Dragomir A, Lim J, Sun Y, Wong KF, Thakor NV, Bezerianos A. Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue. Hum Brain Mapp 2018; 39:3528-3545. [PMID: 29691949 DOI: 10.1002/hbm.24192] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 03/29/2018] [Accepted: 04/05/2018] [Indexed: 12/22/2022] Open
Abstract
Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.
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Affiliation(s)
- Fumihiko Taya
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore.,Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Stavros I Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroinformatics Group, (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Andrei Dragomir
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Julian Lim
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore
| | - Yu Sun
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Kian Foong Wong
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Nitish V Thakor
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,Department of Electrical & Computer Engineering, National University of Singapore, Singapore.,Department of Biomedical Engineering, National University of Singapore, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Anastasios Bezerianos
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,School of Medicine, University of Patras, Greece
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50
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Zimmermann J, Perry A, Breakspear M, Schirner M, Sachdev P, Wen W, Kochan NA, Mapstone M, Ritter P, McIntosh AR, Solodkin A. Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models. NEUROIMAGE-CLINICAL 2018; 19:240-251. [PMID: 30035018 PMCID: PMC6051478 DOI: 10.1016/j.nicl.2018.04.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 04/05/2018] [Accepted: 04/14/2018] [Indexed: 01/09/2023]
Abstract
Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes. Modeled local and global dynamics correlate with individual cognition in Alzheimer's. Proof of concept of The Virtual Brain to characterize individual dynamics Brain-behaviour relations depend on the network modeled (whole brain or limbic). Model parameters predict cognition better than metrics of neuroimaging data.
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Affiliation(s)
- J Zimmermann
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada.
| | - A Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - M Breakspear
- Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Service, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia
| | - M Schirner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - P Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - W Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - N A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - M Mapstone
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
| | - P Ritter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - A R McIntosh
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada
| | - A Solodkin
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
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